getwd()
## [1] "C:/Users/yigit.hakan/Downloads"
# Load tidyverse package
library(tidyverse)
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
library(purrr)
library(leaflet)
## Warning: package 'leaflet' was built under R version 3.4.3
library(maps)
## Warning: package 'maps' was built under R version 3.4.3
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
library(tidyverse)
library(dplyr)
library(sp)
## Warning: package 'sp' was built under R version 3.4.3
library(maptools)
## Warning: package 'maptools' was built under R version 3.4.3
## Checking rgeos availability: TRUE
# Load the data
mortality <- read.csv("mortality_life_expectancy.csv")
# lets see how our data looks like by glimpse function.
glimpse(mortality)
## Observations: 15,106
## Variables: 15
## $ ï..country_code <fctr> SI, SI, SI, SI, SI, SI, SI, SI, ...
## $ country_name <fctr> Slovenia, Slovenia, Slovenia, Sl...
## $ year <int> 2036, 2022, 2023, 2024, 2025, 202...
## $ infant_mortality <dbl> 3.39, 3.76, 3.73, 3.70, 3.67, 3.6...
## $ infant_mortality_male <dbl> 3.76, 4.22, 4.18, 4.14, 4.10, 4.0...
## $ infant_mortality_female <dbl> 3.00, 3.27, 3.25, 3.22, 3.20, 3.1...
## $ life_expectancy <dbl> 80.90, 79.11, 79.26, 79.40, 79.55...
## $ life_expectancy_male <dbl> 77.51, 75.58, 75.73, 75.89, 76.04...
## $ life_expectancy_female <dbl> 84.52, 82.89, 83.02, 83.15, 83.29...
## $ mortality_rate_under5 <dbl> 3.93, 4.43, 4.39, 4.35, 4.31, 4.2...
## $ mortality_rate_under5_male <dbl> 4.39, 5.02, 4.97, 4.91, 4.86, 4.8...
## $ mortality_rate_under5_female <dbl> 3.44, 3.81, 3.78, 3.74, 3.71, 3.6...
## $ mortality_rate_1to4 <dbl> 0.54, 0.68, 0.67, 0.65, 0.64, 0.6...
## $ mortality_rate_1to4_male <dbl> 0.63, 0.80, 0.79, 0.77, 0.76, 0.7...
## $ mortality_rate_1to4_female <dbl> 0.44, 0.54, 0.53, 0.52, 0.51, 0.5...
summary(mortality)
## ï..country_code country_name year infant_mortality
## BT : 101 Bhutan : 101 Min. :1950 Min. : 1.15
## DJ : 101 Djibouti: 101 1st Qu.:2001 1st Qu.: 5.83
## NI : 98 Nigeria : 98 Median :2017 Median : 14.06
## GV : 96 Guinea : 96 Mean :2017 Mean : 29.68
## CB : 89 Cambodia: 89 3rd Qu.:2034 3rd Qu.: 38.77
## CD : 87 Chad : 87 Max. :2050 Max. :298.30
## (Other):14534 (Other) :14534
## infant_mortality_male infant_mortality_female life_expectancy
## Min. : 0.22 Min. : 0.73 Min. :10.01
## 1st Qu.: 6.37 1st Qu.: 5.17 1st Qu.:66.27
## Median : 15.68 Median : 12.34 Median :74.30
## Mean : 32.24 Mean : 27.02 Mean :71.31
## 3rd Qu.: 42.85 3rd Qu.: 34.55 3rd Qu.:79.34
## Max. :314.65 Max. :281.22 Max. :91.58
##
## life_expectancy_male life_expectancy_female mortality_rate_under5
## Min. : 9.82 Min. :10.21 Min. : 1.44
## 1st Qu.:63.77 1st Qu.:68.67 1st Qu.: 6.92
## Median :71.61 Median :77.18 Median : 17.05
## Mean :68.88 Mean :73.85 Mean : 43.18
## 3rd Qu.:76.60 3rd Qu.:82.32 3rd Qu.: 52.62
## Max. :88.69 Max. :94.97 Max. :455.12
##
## mortality_rate_under5_male mortality_rate_under5_female
## Min. : 1.66 Min. : 1.210
## 1st Qu.: 7.63 1st Qu.: 6.103
## Median : 19.11 Median : 15.030
## Mean : 46.13 Mean : 40.120
## 3rd Qu.: 57.40 3rd Qu.: 47.398
## Max. :466.23 Max. :443.650
##
## mortality_rate_1to4 mortality_rate_1to4_male mortality_rate_1to4_female
## Min. : 0.20 Min. : 0.21 Min. : 0.0000
## 1st Qu.: 1.01 1st Qu.: 1.13 1st Qu.: 0.8625
## Median : 2.90 Median : 3.23 Median : 2.5300
## Mean : 14.90 Mean : 15.41 Mean : 14.3698
## 3rd Qu.: 13.91 3rd Qu.: 14.91 3rd Qu.: 12.8375
## Max. :335.59 Max. :342.79 Max. :328.3100
##
#Mortality rate infants for male/ female
qplot(x = infant_mortality_male, y = infant_mortality_female, data = mortality)
#Mortality rate infants - 1970/2000/2017/2050
target <- c("1970", "2000", "2017", "2050")
years_chosen <- filter(mortality, year %in% target)
qplot(x = infant_mortality, y = year, data = years_chosen)
#Mortality rate infants - Turkey
mortality_turkey <- filter(mortality, country_name == "Turkey")
ggplot(aes(x = infant_mortality, y = year), data = mortality_turkey)+
geom_point()
#Life expectancy
qplot(x=life_expectancy, data=mortality, binwidth=5)
#Life expectancy male/female
ggplot(aes(x = life_expectancy_male, y = life_expectancy_female), data = mortality)+
geom_point()
#Life expectancy of Turkey
summary(mortality %>%
filter(country_name == "Turkey"))
## ï..country_code country_name year infant_mortality
## TU :71 Turkey :71 Min. :1980 Min. : 6.63
## AA : 0 Afghanistan : 0 1st Qu.:1998 1st Qu.:10.47
## AC : 0 Albania : 0 Median :2015 Median :18.87
## AE : 0 Algeria : 0 Mean :2015 Mean :27.00
## AF : 0 American Samoa: 0 3rd Qu.:2032 3rd Qu.:41.30
## AG : 0 Andorra : 0 Max. :2050 Max. :72.58
## (Other): 0 (Other) : 0
## infant_mortality_male infant_mortality_female life_expectancy
## Min. : 7.18 Min. : 6.050 Min. :62.61
## 1st Qu.:11.31 1st Qu.: 9.585 1st Qu.:68.73
## Median :20.13 Median :17.550 Median :74.57
## Mean :28.48 Mean :25.455 Mean :73.34
## 3rd Qu.:42.60 3rd Qu.:39.950 3rd Qu.:78.09
## Max. :77.81 Max. :67.090 Max. :80.60
##
## life_expectancy_male life_expectancy_female mortality_rate_under5
## Min. :60.86 Min. :64.45 Min. : 7.47
## 1st Qu.:67.02 1st Qu.:70.52 1st Qu.:11.85
## Median :72.26 Median :77.00 Median :21.51
## Mean :71.14 Mean :75.64 Mean :31.74
## 3rd Qu.:75.54 3rd Qu.:80.78 3rd Qu.:48.09
## Max. :77.89 Max. :83.44 Max. :90.23
##
## mortality_rate_under5_male mortality_rate_under5_female
## Min. : 8.05 Min. : 6.85
## 1st Qu.:12.70 1st Qu.:10.96
## Median :22.61 Median :20.36
## Mean :32.99 Mean :30.42
## 3rd Qu.:48.81 3rd Qu.:47.34
## Max. :95.08 Max. :85.14
##
## mortality_rate_1to4 mortality_rate_1to4_male mortality_rate_1to4_female
## Min. : 0.840 Min. : 0.880 Min. : 0.800
## 1st Qu.: 1.395 1st Qu.: 1.400 1st Qu.: 1.390
## Median : 2.690 Median : 2.530 Median : 2.860
## Mean : 4.963 Mean : 4.745 Mean : 5.191
## 3rd Qu.: 7.075 3rd Qu.: 6.480 3rd Qu.: 7.695
## Max. :19.030 Max. :18.730 Max. :19.350
##
#Life expectancy of 1970/2000/2017/2050
target2 <- c("1970", "2000", "2017", "2050")
years_chosen2 <- filter(mortality, year %in% target2)
qplot(x = life_expectancy, y = year, data = years_chosen2)
mydata<-read.csv(file="midyear_population.csv", header=TRUE)
data17<-mydata%>%
filter(year=="2017")%>%
group_by(country_name,per_area,latitude,longitude)
data1950<-mydata%>%
filter(year=="1950")%>%
group_by(country_name,per_area,latitude,longitude)
data2050<-mydata%>%
filter(year=="2050")%>%
group_by(country_name,per_area,latitude,longitude)
m=map("world", fill = TRUE, plot = FALSE)
m_nms <- sapply( strsplit( m$names, ':' ), function(x) x[1] )
m_poli <- map2SpatialPolygons(m, IDs=m_nms, proj4string=CRS("+proj=longlat +datum=WGS84"))
m_df <- data.frame(ID = names(m_poli))
rownames(m_df) <- names(m_poli)
world_spdf <- SpatialPolygonsDataFrame(m_poli, m_df)
birlestirme17<- merge(world_spdf, data17, by.x = 'ID', by.y = 'country_name')
birlestirme1950<- merge(world_spdf, data1950, by.x = 'ID', by.y = 'country_name')
birlestirme2050<- merge(world_spdf, data2050, by.x = 'ID', by.y = 'country_name')
bins <- c(0, 10, 20, 50, 100, 200, 500, 1000, Inf)
pal1<- colorBin("YlOrRd", domain = birlestirme1950$per_area, bins = bins)
pal2<- colorBin("YlOrRd", domain = birlestirme17$per_area, bins = bins)
pal3<- colorBin("YlOrRd", domain = birlestirme2050$per_area, bins = bins)
#alternative for continious coloring
#qpal <- colorQuantile("Blues", data17$per_area, n = 9)
labels1<- sprintf(
"<strong>%s</strong><br/>%g people / km<sup>2</sup>",
birlestirme1950$ID, birlestirme1950$per_area
) %>% lapply(htmltools::HTML)
labels2<- sprintf(
"<strong>%s</strong><br/>%g people / km<sup>2</sup>",
birlestirme17$ID, birlestirme17$per_area
) %>% lapply(htmltools::HTML)
labels3<- sprintf(
"<strong>%s</strong><br/>%g people / km<sup>2</sup>",
birlestirme2050$ID, birlestirme2050$per_area
) %>% lapply(htmltools::HTML)
leaflet(data = birlestirme1950) %>% addTiles() %>%
setView(lat = 39, lng = 35, zoom = 1) %>%
addPolygons(color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.5,
fillColor = ~pal1(birlestirme1950$per_area),
highlightOptions = highlightOptions(color = "white", weight = 2,bringToFront = TRUE),
label=labels1,
labelOptions=labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))%>%
addLegend("bottomright", pal=pal1, values = ~birlestirme1950$per_area,
title = "per area",
labFormat = labelFormat(digits=5),
opacity = 1)
leaflet(data = birlestirme17) %>% addTiles() %>%
setView(lat = 39, lng = 35, zoom = 1) %>%
addPolygons(color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.5,
fillColor = ~pal2(birlestirme17$per_area),
highlightOptions = highlightOptions(color = "white", weight = 2,bringToFront = TRUE),
label=labels2,
labelOptions=labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))%>%
addLegend("bottomright", pal=pal2, values = ~birlestirme17$per_area,
title = "per area",
labFormat = labelFormat(digits=5),
opacity = 1)
leaflet(data = birlestirme2050) %>% addTiles() %>%
setView(lat = 39, lng = 35, zoom = 1) %>%
addPolygons(color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.5,
fillColor = ~pal3(birlestirme2050$per_area),
highlightOptions = highlightOptions(color = "white", weight = 2,bringToFront = TRUE),
label=labels3,
labelOptions=labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "15px",
direction = "auto"))%>%
addLegend("bottomright", pal=pal3, values = ~birlestirme2050$per_area,
title = "per area",
labFormat = labelFormat(digits=5),
opacity = 1)